Towards time series aggregation with exact error quantification for optimization of energy systems

Energy system optimization models are becoming increasingly popular for analyzing energy markets, such as the impact of new policies or interactions between energy carriers. One key challenge of these models is the trade-off between modeling accuracy and computational tractability. A recently proposed mathematical framework addresses this challenge by achieving exact time series aggregations merging time periods sharing the same active constraint sets. This aggregation, however, is insufficient when the number of unique active constraints is large. We overcome this issue by aggregating data points from different active constraint sets. While this further reduces model size, it inevitably introduces an error compared to the full model. Yet, we show how this error can be exactly quantified without re-solving the optimization problem, enabling users to trade off computational efficiency and model accuracy proactively. This may be especially useful in energy markets to accommodate varying granularity across short- and long-term time horizons.